First-Principles Simulations of Inelastic Electron Tunneling Spectroscopyof Molecular Junctions

A generalized Green's function theory is developed to simulate the inelastic electron tunneling spectroscopy (IETS) of molecular junctions. It has been applied to a realistic molecular junction with an octanedithiolate embedded between two gold contacts in combination with the hybrid density functional theory calculations. The calculated spectra are in excellent agreement with recent experimental results. Strong temperature dependence of the experimental IETS spectra is also reproduced. It is shown that the IETS is extremely sensitive to the intra-molecular conformation and to the molecule-metal contact geometry

Bi-collinear antiferromagnetic order in the tetragonal α\alpha-FeTe

By the first-principles electronic structure calculations, we find that the ground state of PbO-type tetragonal $\alpha$-FeTe is in a bi-collinear antiferromagnetic state, in which the Fe local moments ($\sim2.5\mu_B$) are ordered ferromagnetically along a diagonal direction and antiferromagnetically along the other diagonal direction on the Fe square lattice. This bi-collinear order results from the interplay among the nearest, next nearest, and next next nearest neighbor superexchange interactions $J_1$, $J_2$, and $J_3$, mediated by Te $5p$-band. In contrast, the ground state of $\alpha$-FeSe is in the collinear antiferromagnetic order, similar as in LaFeAsO and BaFe$_2$As$_2$.Comment: 5 pages and 5 figure

In-Process Global Interpretation for Graph Learning via Distribution Matching

Graphs neural networks (GNNs) have emerged as a powerful graph learning model due to their superior capacity in capturing critical graph patterns. To gain insights about the model mechanism for interpretable graph learning, previous efforts focus on post-hoc local interpretation by extracting the data pattern that a pre-trained GNN model uses to make an individual prediction. However, recent works show that post-hoc methods are highly sensitive to model initialization and local interpretation can only explain the model prediction specific to a particular instance. In this work, we address these limitations by answering an important question that is not yet studied: how to provide global interpretation of the model training procedure? We formulate this problem as in-process global interpretation, which targets on distilling high-level and human-intelligible patterns that dominate the training procedure of GNNs. We further propose Graph Distribution Matching (GDM) to synthesize interpretive graphs by matching the distribution of the original and interpretive graphs in the feature space of the GNN as its training proceeds. These few interpretive graphs demonstrate the most informative patterns the model captures during training. Extensive experiments on graph classification datasets demonstrate multiple advantages of the proposed method, including high explanation accuracy, time efficiency and the ability to reveal class-relevant structure.Comment: Under Revie